Modeling the Pedestrian Flow Before Bottleneck Through Learning-Based Method-Reference-Cited by-同舟云学术

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Modeling the Pedestrian Flow Before Bottleneck Through Learning-Based Method

Published:2023-07 Issue:7 Volume:24 Page:7035-7047
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Jiang Nan¹^ORCID,Yang Lizhong¹,Yuen Richard Kwok Kit²,Zhai Chunjie³

Affiliation:

1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, China

2. Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

3. Department of Information Technology, Nanjing Forest Police College, Nanjing, China

Funder

Research Grant Council of the Hong Kong Special Administrative Region, China

Opening Fund of the State Key Laboratory of Fire Science

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10175858/10077452.pdf?arnumber=10077452

Reference47 articles.

1. SSeg-LSTM: Semantic Scene Segmentation for Trajectory Prediction

2. A floor field cellular automaton for crowd evacuation considering different walking abilities

3. SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction

4. Simulation of Influence of Desired Velocity Distribution and Herd Effect on Pedestrian Evacuation

5. Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

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